{
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 心电分类标准(AAMI)\n",
    "\n",
    "1.AAMI EC57标准简介\n",
    "ANSL／AAMI EC57：2012 是由美国医疗仪器促进协会(Association for the Advancement of Medical Instrumentation，AAMI)制定的一系列标准，包括心率失常的分类、心律失常分类检测算法的评估标准等等。\n",
    "\n",
    "AAMI规定心电节拍可以分为五大类：\n",
    "- N(正常或者束支传导阻滞节拍)\n",
    "- s(室上性异常节拍)\n",
    "- V(心室异常节拍)\n",
    "- F(融合节拍)\n",
    "- Q(未能分类的节拍)\n",
    "以及无法识读的u类、辅助无法识读标志的x类和0类\n",
    "\n",
    "AAMI还规定了心律失常分类检测算法的评估标准，包括算法准确率的计算方法（混淆矩阵），以及准确率(Acc)、灵敏度(sen)、真阳性率(Ppr)等作为衡量分类器分类性能的参数。通过混淆矩阵计算各个参数。\n",
    "\n",
    "AAMI标准中指出Q类分类的准确率仅供参考，而u、x、0类不是真正的心电节拍，因此最终衡量分类算法的准确率是N、s、V、F类的心电节拍的Acc、Sen、Ppr。\n",
    "\n",
    "2.为什么要应用AAMI标准\n",
    "规范化，便于横向比较\n",
    "目前发表的关于心电信号自动分类的论文中，有很大一部分没有应用AAMI标准，而是任意选取5-9个MIT-BIH中数量最多的心拍种类进行分类，导致无法科学有效地横向比较各个算法的优劣，这是当前心电分类领域存在的一个很严重的问题。如果相关研究人员都能应用AAMI标准下的分类标准和评估标准，并且统一使用MIT-BIH中同样的数据作为实验数据，各个算法的横向比较将变得容易很多。\n",
    "\n",
    "3.MIT-BIH数据库的心拍类型 vs AAMI规定的心电种类\n",
    "AAMI规定的心电种类和MIT- BIH 心电数据库注释的心拍类型是两种临床上的分类方法，因此需要把MIT- BIH 数据库注释的心电节拍类型转换为AAMI的心电种类，转换表格如下\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "原文链接：https://blog.csdn.net/amilylwy/article/details/53446143"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将Mit数据集中的19类心拍标签转化为AAMI中的五类\n",
    "\n",
    "1.原因:以上是所查阅资料的原因,以下是小组经过思考所得的:\n",
    "(1)AAMI分类仅有五类,而Mit数据集中的心拍标签却有19类之多,做深度学习肯定分的类别越少,最终预测的结果也就越准确\n",
    "(2)和以上资料所说的一样,AAMI分类比Mit数据集分类的普适性高,使用程度高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N = 75021\n",
      "f = 982\n",
      "e = 16\n",
      "/ = 7025\n",
      "j = 229\n",
      "n = 0\n",
      "B = 0\n",
      "L = 8071\n",
      "R = 7256\n",
      "S = 2\n",
      "A = 2546\n",
      "J = 83\n",
      "a = 150\n",
      "V = 7129\n",
      "E = 106\n",
      "r = 0\n",
      "F = 802\n",
      "Q = 33\n",
      "? = 0\n"
     ]
    }
   ],
   "source": [
    "import wfdb\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "AAMI_MIT  = {'N': 'Nfe/jnBLR',#将19类信号分为五大类\n",
    "             'S': 'SAJa',\n",
    "             'V': 'VEr',\n",
    "             'F': 'F',\n",
    "             'Q': 'Q?'}\n",
    "# 5分类存储字典\n",
    "AAMI = {'N': [],'S': [],'V': [],'F': [], 'Q': []}\n",
    "file_name =['100', '101', '102', '103', '104', '105', '106', '107',\n",
    "            '108', '109', '111', '112', '113', '114', '115', '116',\n",
    "            '117', '118', '119', '121', '122', '123', '124', '200',\n",
    "            '201', '202', '203', '205', '207', '208', '209', '210',\n",
    "            '212', '213', '214', '215', '217', '219', '220', '221',\n",
    "            '222', '223', '228', '230', '231', '232', '233', '234']\n",
    "ECG = {'N': [], 'f': [], 'e': [], '/': [], 'j': [], 'n': [], 'B': [],\n",
    "       'L': [], 'R': [], 'S': [], 'A': [], 'J': [], 'a': [], 'V': [],\n",
    "       'E': [], 'r': [], 'F': [], 'Q': [], '?': []}\n",
    "\n",
    "for F_name in file_name:\n",
    "    signal_annotation = wfdb.rdann(f'../Datasets/mit-bih-arrhythmia-database-1.0.0/{F_name}', \\\n",
    "                                   \"atr\", sampfrom=0, sampto=650000)\n",
    "    record = wfdb.rdrecord(f'..\\Datasets\\mit-bih-arrhythmia-database-1.0.0/{F_name}',\\\n",
    "                            sampfrom=0, sampto=650000, physical=True, channels=[0, ])\n",
    "    # 删除非R点的标签\n",
    "    ECG_R_list = np.array(['N', 'f', 'e', '/', 'j', 'n', 'B',\n",
    "                           'L', 'R', 'S', 'A', 'J', 'a', 'V',\n",
    "                           'E', 'r', 'F', 'Q', '?'])\n",
    "    # 获取表示R点的心拍类型的索引\n",
    "    Index = np.isin(signal_annotation.symbol, ECG_R_list)\n",
    "    # 将标签从list列表类型转化为数组类型为了用下面的索引值提取\n",
    "    Label = np.array(signal_annotation.symbol)\n",
    "    # 提取表示为R点的心拍标签\n",
    "    Label = Label[Index]\n",
    "    # 提取表示为R点的坐标\n",
    "    Sample = signal_annotation.sample[Index]\n",
    "    # 获取心拍种类\n",
    "    Label_kind = list(set(Label))\n",
    "    # 读取每一种R点在信号中的位置\n",
    "    for k in Label_kind:\n",
    "        index = [i for i, x in enumerate(Label) if x == k]\n",
    "        Signal_index = Sample[index]\n",
    "        length = len(record.p_signal)\n",
    "        #print(Signal_index)\n",
    "        #截取\n",
    "        for site in Signal_index:\n",
    "            if 130 < site < length-130:\n",
    "                ECG_signal = record.p_signal.flatten().tolist()[site-130:site+130]\n",
    "                #print(ECG_signal)\n",
    "                ECG[str(k)].append(ECG_signal)\n",
    "\n",
    "# 打印种类\n",
    "for key, value in ECG.items():\n",
    "    print(f'{key} = {len(value)}')\n",
    "\n",
    "for ECG_key, ECG_value in ECG.items():\n",
    "    for AAMI_MIT_key, AAMI_MIT_value in AAMI_MIT.items():\n",
    "        if ECG_key in AAMI_MIT_value:\n",
    "            AAMI[AAMI_MIT_key].extend(ECG_value)\n",
    "\n",
    "# 5分类并保存到CSV格式的文件里\n",
    "for key, value in AAMI.items():\n",
    "    with open(f'./AAMI/{key}.csv', 'w',newline='\\n') as f:\n",
    "        writer = csv.writer(f)\n",
    "        # 将列表的每条数据依次写入csv文件， 并以逗号分隔\n",
    "        # 传入的数据为列表中嵌套列表或元组，每一个列表或元组为每一行的数据\n",
    "        writer.writerows(value)\n",
    "\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "AAMI_key = {'N','S','V','F','Q'}\n",
    "AAMI = dict()\n",
    "\n",
    "def Tolist(x):\n",
    "    return list(map(float,x))\n",
    "\n",
    "for key in AAMI_key:\n",
    "    with open(f'{key}.csv', 'r') as f:\n",
    "        reader = csv.reader(f)\n",
    "        AAMI[key] = list(map(Tolist,list(reader)))\n",
    "\n",
    "for i in AAMI_key:\n",
    "    print(f'{i}={np.array(AAMI[i]).shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将五类心电滤波后存入新的文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "f1 = pd.read_csv(\"F.csv\")\n",
    "f2 = pd.read_csv(\"N.csv\")\n",
    "f3 = pd.read_csv(\"Q.csv\")\n",
    "f4 = pd.read_csv(\"S.csv\")\n",
    "f5 = pd.read_csv(\"V.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "import numpy as np\n",
    "\n",
    "def butterwoth_fliter(data, frequency=360, highpass=40, lowpass=0.1):\n",
    "    [b, a] = signal.butter(3, [lowpass / frequency * 2, highpass / frequency * 2], 'bandpass')\n",
    "    Signal_pro = signal.filtfilt(b, a, data)\n",
    "    return Signal_pro\n",
    "\n",
    "filter_F = np.zeros(f1.shape)\n",
    "filter_N = np.zeros(f2.shape)\n",
    "filter_Q = np.zeros(f3.shape)\n",
    "filter_S = np.zeros(f4.shape)\n",
    "filter_V = np.zeros(f5.shape)\n",
    "\n",
    "for i in range(0,f1.shape[0]):\n",
    "    fliter_ecg = butterwoth_fliter(f1.iloc[i])#滤波\n",
    "    filter_F[i] = fliter_ecg\n",
    "for i in range(0,f2.shape[0]):\n",
    "    fliter_ecg = butterwoth_fliter(f2.iloc[i])#滤波\n",
    "    filter_N[i] = fliter_ecg\n",
    "for i in range(0,f3.shape[0]):\n",
    "    fliter_ecg = butterwoth_fliter(f3.iloc[i])#滤波\n",
    "    filter_Q[i] = fliter_ecg\n",
    "for i in range(0,f4.shape[0]):\n",
    "    fliter_ecg = butterwoth_fliter(f4.iloc[i])#滤波\n",
    "    filter_S[i] = fliter_ecg\n",
    "for i in range(0,f5.shape[0]):\n",
    "    fliter_ecg = butterwoth_fliter(f5.iloc[i])#滤波\n",
    "    filter_V[i] = fliter_ecg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "# for key in AAMI_key:\n",
    "with open(f'./filtered_butterworth/F_filter.csv', 'w',newline='\\n') as f: #./filered_butterworth/F_filter.csv\n",
    "    writer = csv.writer(f)\n",
    "    # 将列表的每条数据依次写入csv文件， 并以逗号分隔\n",
    "    # 传入的数据为列表中嵌套列表或元组，每一个列表或元组为每一行的数据\n",
    "    writer.writerows(filter_F)\n",
    "with open(f'./filtered_butterworth/N_filter.csv', 'w',newline='\\n') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(filter_N)\n",
    "with open(f'./filtered_butterworth/Q_filter.csv', 'w',newline='\\n') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(filter_Q)\n",
    "with open(f'./filtered_butterworth/S_filter.csv', 'w',newline='\\n') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(filter_S)\n",
    "with open(f'./filtered_butterworth/V_filter.csv', 'w',newline='\\n') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(filter_V)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mit数据集读取并进行预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pywt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 小波去噪预处理\n",
    "def denoise(data):\n",
    "    # 小波变换\n",
    "    coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)\n",
    "    cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs\n",
    "    # 阈值去噪\n",
    "    threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))\n",
    "    cD1.fill(0)\n",
    "    cD2.fill(0)\n",
    "    for i in range(1, len(coeffs) - 2):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], threshold)\n",
    "    # 小波反变换,获取去噪后的信号\n",
    "    rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')\n",
    "    return rdata\n",
    "\n",
    "record = wfdb.rdrecord('../Datasets/mit-bih-arrhythmia-database-1.0.0/' + '100', channel_names=['MLII'])\n",
    "data = record.p_signal.flatten()\n",
    "rdata = denoise(data=data)\n",
    "# 获取心电数据记录中R波的位置和对应的标签\n",
    "annotation = wfdb.rdann('../Datasets/mit-bih-arrhythmia-database-1.0.0/' + '100', 'atr')\n",
    "Rlocation = annotation.sample\n",
    "Rclass = annotation.symbol\n",
    "plt.plot(data[:3600])\n",
    "plt.plot(rdata[:3600])\n",
    "Rlocation_1 = Rlocation[Rlocation <= 3600]\n",
    "plt.plot(Rlocation_1,data[:3600][Rlocation_1],'ro')\n",
    "print(Rclass)\n",
    "print(Rlocation)\n",
    "# print(Rclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wfdb\n",
    "import pywt\n",
    "import seaborn\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 测试集在数据集中所占的比例\n",
    "RATIO = 0.2\n",
    "\n",
    "# 读取滤波后的心电数据和对应标签\n",
    "numberSet = ['F_filter','N_filter','Q_filter','S_filter','V_filter']\n",
    "# numberSet_test = ['F_filter1']\n",
    "lableSet = []\n",
    "dataSet_last = np.empty((0,260))\n",
    "# lableSet = ['F','N','Q','S','V']\n",
    "for i in numberSet:\n",
    "    dataSet = pd.read_csv('./filtered_butterworth/{}.csv'.format(i)).to_numpy()\n",
    "    for j in range(0,dataSet.shape[0]):\n",
    "        lableSet.append(numberSet.index(i))\n",
    "    dataSet = np.concatenate((dataSet,dataSet_last),axis=0)\n",
    "    dataSet_last = dataSet\n",
    "lableSet = np.array(lableSet).reshape(-1,1)\n",
    "train_ds = np.hstack((dataSet, lableSet))\n",
    "np.random.shuffle(train_ds)\n",
    "# 数据集及其标签集\n",
    "X = train_ds[:, :260].reshape(-1, 260, 1)\n",
    "Y = train_ds[:, 260]\n",
    "# 测试集及其标签集\n",
    "shuffle_index = np.random.permutation(len(X))\n",
    "# 设定测试集的大小 RATIO是测试集在数据集中所占的比例\n",
    "test_length = int(RATIO * len(shuffle_index))\n",
    "# 测试集的长度\n",
    "test_index = shuffle_index[:test_length]\n",
    "# 训练集的长度\n",
    "train_index = shuffle_index[test_length:]\n",
    "X_test, Y_test = X[test_index], Y[test_index]\n",
    "X_train, Y_train = X[train_index], Y[train_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21888, 260, 1)\n",
      "(87553, 260, 1)\n",
      "(21888,)\n",
      "(87553,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(X_train.shape)\n",
    "print(Y_test.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87553, 260, 1)\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 260, 4)            88        \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1D  (None, 130, 4)           0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 130, 16)           1488      \n",
      "                                                                 \n",
      " max_pooling1d_1 (MaxPooling  (None, 65, 16)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 65, 32)            12832     \n",
      "                                                                 \n",
      " average_pooling1d (AverageP  (None, 33, 32)           0         \n",
      " ooling1D)                                                       \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           (None, 33, 64)            55360     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2112)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               270464    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 5)                 645       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 340,877\n",
      "Trainable params: 340,877\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/30\n",
      "548/548 [==============================] - 25s 44ms/step - loss: 0.1384 - accuracy: 0.9603 - val_loss: 0.0597 - val_accuracy: 0.9798\n",
      "Epoch 2/30\n",
      "548/548 [==============================] - 27s 49ms/step - loss: 0.0593 - accuracy: 0.9806 - val_loss: 0.0452 - val_accuracy: 0.9850\n",
      "Epoch 3/30\n",
      "548/548 [==============================] - 26s 47ms/step - loss: 0.0491 - accuracy: 0.9826 - val_loss: 0.0475 - val_accuracy: 0.9834\n",
      "Epoch 4/30\n",
      "548/548 [==============================] - 25s 45ms/step - loss: 0.0431 - accuracy: 0.9850 - val_loss: 0.0374 - val_accuracy: 0.9870\n",
      "Epoch 5/30\n",
      "548/548 [==============================] - 25s 45ms/step - loss: 0.0378 - accuracy: 0.9863 - val_loss: 0.0349 - val_accuracy: 0.9871\n",
      "Epoch 6/30\n",
      "548/548 [==============================] - 28s 51ms/step - loss: 0.0361 - accuracy: 0.9861 - val_loss: 0.0351 - val_accuracy: 0.9869\n",
      "Epoch 7/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0333 - accuracy: 0.9873 - val_loss: 0.0404 - val_accuracy: 0.9870\n",
      "Epoch 8/30\n",
      "548/548 [==============================] - 24s 45ms/step - loss: 0.0318 - accuracy: 0.9883 - val_loss: 0.0366 - val_accuracy: 0.9865\n",
      "Epoch 9/30\n",
      "548/548 [==============================] - 24s 45ms/step - loss: 0.0300 - accuracy: 0.9883 - val_loss: 0.0339 - val_accuracy: 0.9898\n",
      "Epoch 10/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0295 - accuracy: 0.9888 - val_loss: 0.0304 - val_accuracy: 0.9893\n",
      "Epoch 11/30\n",
      "548/548 [==============================] - 26s 47ms/step - loss: 0.0277 - accuracy: 0.9888 - val_loss: 0.0312 - val_accuracy: 0.9898\n",
      "Epoch 12/30\n",
      "548/548 [==============================] - 25s 45ms/step - loss: 0.0263 - accuracy: 0.9898 - val_loss: 0.0325 - val_accuracy: 0.9894\n",
      "Epoch 13/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0260 - accuracy: 0.9899 - val_loss: 0.0279 - val_accuracy: 0.9902\n",
      "Epoch 14/30\n",
      "548/548 [==============================] - 23s 43ms/step - loss: 0.0245 - accuracy: 0.9907 - val_loss: 0.0326 - val_accuracy: 0.9883\n",
      "Epoch 15/30\n",
      "548/548 [==============================] - 25s 45ms/step - loss: 0.0249 - accuracy: 0.9906 - val_loss: 0.0329 - val_accuracy: 0.9886\n",
      "Epoch 16/30\n",
      "548/548 [==============================] - 25s 46ms/step - loss: 0.0232 - accuracy: 0.9909 - val_loss: 0.0294 - val_accuracy: 0.9905\n",
      "Epoch 17/30\n",
      "548/548 [==============================] - 26s 47ms/step - loss: 0.0223 - accuracy: 0.9915 - val_loss: 0.0267 - val_accuracy: 0.9907\n",
      "Epoch 18/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0206 - accuracy: 0.9920 - val_loss: 0.0277 - val_accuracy: 0.9917\n",
      "Epoch 19/30\n",
      "548/548 [==============================] - 24s 43ms/step - loss: 0.0186 - accuracy: 0.9927 - val_loss: 0.0334 - val_accuracy: 0.9900\n",
      "Epoch 20/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0192 - accuracy: 0.9928 - val_loss: 0.0293 - val_accuracy: 0.9912\n",
      "Epoch 21/30\n",
      "548/548 [==============================] - 28s 51ms/step - loss: 0.0195 - accuracy: 0.9925 - val_loss: 0.0270 - val_accuracy: 0.9910\n",
      "Epoch 22/30\n",
      "548/548 [==============================] - 29s 53ms/step - loss: 0.0180 - accuracy: 0.9930 - val_loss: 0.0278 - val_accuracy: 0.9917\n",
      "Epoch 23/30\n",
      "548/548 [==============================] - 26s 48ms/step - loss: 0.0201 - accuracy: 0.9925 - val_loss: 0.0328 - val_accuracy: 0.9901\n",
      "Epoch 24/30\n",
      "548/548 [==============================] - 25s 45ms/step - loss: 0.0174 - accuracy: 0.9934 - val_loss: 0.0313 - val_accuracy: 0.9925\n",
      "Epoch 25/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0187 - accuracy: 0.9933 - val_loss: 0.0311 - val_accuracy: 0.9926\n",
      "Epoch 26/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0183 - accuracy: 0.9932 - val_loss: 0.0288 - val_accuracy: 0.9929\n",
      "Epoch 27/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0161 - accuracy: 0.9939 - val_loss: 0.0296 - val_accuracy: 0.9921\n",
      "Epoch 28/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0164 - accuracy: 0.9937 - val_loss: 0.0297 - val_accuracy: 0.9908\n",
      "Epoch 29/30\n",
      "548/548 [==============================] - 24s 44ms/step - loss: 0.0151 - accuracy: 0.9942 - val_loss: 0.0336 - val_accuracy: 0.9919\n",
      "Epoch 30/30\n",
      "548/548 [==============================] - 24s 43ms/step - loss: 0.0147 - accuracy: 0.9944 - val_loss: 0.0299 - val_accuracy: 0.9913\n",
      "684/684 [==============================] - 4s 6ms/step\n",
      "[[6.09814229e-21 1.00000000e+00 3.21301694e-36 1.78396539e-22\n",
      "  1.12674804e-14]\n",
      " [4.52617053e-15 1.00000000e+00 6.12604473e-38 1.49857413e-23\n",
      "  3.23777199e-10]\n",
      " [1.51818851e-04 9.99806702e-01 9.69402557e-20 4.58493139e-11\n",
      "  4.15354152e-05]\n",
      " ...\n",
      " [1.17368856e-17 1.00000000e+00 3.91629626e-38 1.45716217e-24\n",
      "  2.30923702e-09]\n",
      " [1.19668078e-12 1.00000000e+00 0.00000000e+00 5.40905702e-25\n",
      "  3.15776161e-09]\n",
      " [3.33460932e-11 1.26790474e-04 2.02756437e-05 9.99847531e-01\n",
      "  5.42078578e-06]]\n"
     ]
    }
   ],
   "source": [
    "# 构建CNN模型\n",
    "def buildModel():\n",
    "    newModel = tf.keras.models.Sequential([\n",
    "        tf.keras.layers.InputLayer(input_shape=(260, 1)),# 第一个卷积层, 4 个 21x1 卷积核\n",
    "        tf.keras.layers.Conv1D(filters=4, kernel_size=21, strides=1, padding='SAME', activation='tanh'),# 第一个池化层, 最大池化,4 个 3x1 卷积核, 步长为 2\n",
    "        tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),# 第二个卷积层, 16 个 23x1 卷积核\n",
    "        tf.keras.layers.Conv1D(filters=16, kernel_size=23, strides=1, padding='SAME', activation='relu'),# 第二个池化层, 最大池化,4 个 3x1 卷积核, 步长为 2\n",
    "        tf.keras.layers.MaxPool1D(pool_size=3, strides=2, padding='SAME'),# 第三个卷积层, 32 个 25x1 卷积核\n",
    "        tf.keras.layers.Conv1D(filters=32, kernel_size=25, strides=1, padding='SAME', activation='tanh'),# 第三个池化层, 平均池化,4 个 3x1 卷积核, 步长为 2\n",
    "        tf.keras.layers.AvgPool1D(pool_size=3, strides=2, padding='SAME'), # 第四个卷积层, 64 个 27x1 卷积核\n",
    "        tf.keras.layers.Conv1D(filters=64, kernel_size=27, strides=1, padding='SAME', activation='relu'),# 打平层,方便全连接层处理'\n",
    "        tf.keras.layers.Flatten(),# 全连接层,128 个节点 转换成128个节点\n",
    "        tf.keras.layers.Dense(128, activation='relu'),# Dropout层,dropout = 0.2\n",
    "        tf.keras.layers.Dropout(rate=0.2),# 全连接层,5 个节点\n",
    "        tf.keras.layers.Dense(5, activation='softmax')\n",
    "    ])\n",
    "    return newModel\n",
    "\n",
    "def plotHeatMap(Y_test, Y_pred):\n",
    "    con_mat = confusion_matrix(Y_test, Y_pred)\n",
    "    plt.figure(figsize=(4, 5))\n",
    "    seaborn.heatmap(con_mat, annot=True, fmt='.20g', cmap='Blues')\n",
    "    plt.ylim(0, 5)\n",
    "    plt.xlabel('Predicted labels')\n",
    "    plt.ylabel('True labels')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def main():\n",
    "    # X_train,Y_train为所有的数据集和标签集\n",
    "    # X_test,Y_test为拆分的测试集和标签集\n",
    "    # X_train, Y_train, X_test, Y_test = loadData()\n",
    "    print(X_train.shape)\n",
    "\n",
    "    model = buildModel()\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy', metrics=['accuracy']\n",
    "                  # metrics: 列表，包含评估模型在训练和测试时的性能的指标，典型用法是metrics=[‘accuracy’]。\n",
    "                  )\n",
    "    model.summary()\n",
    "\n",
    "    # 训练与验证\n",
    "    model.fit(X_train, Y_train, epochs=30, batch_size=128, validation_split=RATIO)  # validation_split 训练集所占比例\n",
    "    \n",
    "    # 预测\n",
    "    Y_pred = model.predict(X_test)\n",
    "    print(Y_pred)\n",
    "\n",
    "    model.save(r'./model/Five_classifications.h5')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "684/684 [==============================] - 4s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "# 0.9931\n",
    "# tensorflow有毒,加载模型不能存放在中文路径下面\n",
    "model = tf.keras.models.load_model('./model/Five_classifications.h5')\n",
    "model1 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21888, 5)\n",
      "[[0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " ...\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]]\n",
      "21888\n",
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kMhw4cEBp//DhwyGTyZS2bt26KdVJTEzE4MGDoaenBwMDA3h6eiI1NVWpzvXr1+Hk5AQdHR3UqlULQUFBeWLZu3cvGjVqBB0dHdja2uLQoUPiGgMmDyJSM//9A12UTYy0tDTY29tj1apV763TrVs3xMXFKbaffvpJaf/gwYNx69YthIaGIiQkBGfOnMHo0aMV+1NSUuDq6goLCwuEh4djwYIFmDlzJtavX6+oc+7cOQwcOBCenp64evUq3N3d4e7ujps3b4pqj0wQBEHUEWVARo6qI6Dyprzc26qiZtn/vKhTxJna2uN+kyYQALErPivUcTKZDPv374e7u7uibPjw4UhKSsrTI3knMjISNjY2uHTpElq2bAkAOHLkCHr06IHHjx/D3Nwca9aswffff4/4+HhoaWkBAKZMmYIDBw4gKioKAPDll18iLS0NISEhinO3adMGTZs2xdq1awvchrL/L4mISAQpex6ZmZlISUlR2jIzMwsd26lTp2BiYgIrKyt88803ePnypWJfWFgYDAwMFIkDAFxcXKChoYELFy4o6jg7OysSBwC4ubkhOjoar169UtRxcXFRuq6bmxvCwsJExcrkQURqRcrkERgYCH19faUtMDCwUHF169YN27Ztw/Hjx/HDDz/g9OnT6N69O3JzcwEA8fHxMDExUTqmQoUKqFatGuLj4xV1atSooVTn3euP1Xm3v6C4VJeIqJACAgLg6+urVKatrV2ocw0YMEDxs62tLezs7FCvXj2cOnUKXbp0KVKcxYHJg4jUipTf89DW1i50sviYunXrwtjYGHfv3kWXLl1gamqKZ8+eKdXJyclBYmIiTE1NAQCmpqZISEhQqvPu9cfqvNtfUBy2IiL1oqKlumI9fvwYL1++hJmZGQDA0dERSUlJCA8PV9Q5ceIE5HI5HBwcFHXOnDmD7OxsRZ3Q0FBYWVnB0NBQUef48eNK1woNDYWjo6Oo+Jg8iIhKQGpqKiIiIhAREQEAiImJQUREBGJjY5Gamgo/Pz+cP38eDx48wPHjx9G7d2/Ur18fbm5uAABra2t069YNo0aNwsWLF/HXX3/B29sbAwYMgLm5OQBg0KBB0NLSgqenJ27duoXdu3dj2bJlSkNr48ePx5EjR7Bo0SJERUVh5syZuHz5Mry9vUW1h0t1iQqAS3VLj6Iu1a3rK/4Lce9zf3GPAtc9deoUOnXqlKfcw8MDa9asgbu7O65evYqkpCSYm5vD1dUVc+bMUZrcTkxMhLe3Nw4ePAgNDQ3069cPy5cvR9WqVRV1rl+/Di8vL1y6dAnGxsYYN24c/P39la65d+9eTJ06FQ8ePECDBg0QFBSEHj0K3haAyYOoQJg8So+iJo96Ew9LEwiAe4u6S3ausqbs/0siIqISx9VWRKRWeFNdaTB5EJFa4S3ZpcFhKyIiEo09DyJSK+x4SIPJg4jUCoetpMFhKyIiEo09DyJSK+x4SIPJg4jUioYGs4cUOGxFRESisedBRGqFw1bSYM+DiIhEY/IohF07g9G9a2e0amaLwQO+wI3r11Udkijhly9h3NgxcOnYHvaNrXDi+DFVh1Ropfm9WLd6JVraWStt/T77586lmZmZ+OF/s9HFqQ2cHFrAz+dbvHz5QrE/KekVxo0ZhW5dnOHYwg49u3bCD/PmIDU1VRXNUdizayc+79MLbVs3R9vWzTF00Jc4++dpxf4Xz5/juyl+6OzcDg4tm+LLz/vg2B9HVRixMikfQ6vOmDxEOnL4EBYGBeLrsV7YtXc/rKwa4ZuvPZUeVF/apae/gZWVFQKmzlB1KEVSFt6LuvXq48iJM4pt49Zgxb7FQYE4c/oU5i9civWbt+HF82fw8/lWsV9DQwMdOnXG4uWrse/gYcyYMw8Xz4chcM5MFbTkHyY1TDHeZxJ+2rsPO/f8gtYObTDe2wt3794BAHz/nT8exMRg2co1+GX/QXRx6Qq/iRMQGXlbpXG/I5NJt6kzJg+Rtm/djL6f94d7n36oV78+ps6YBR0dHRzY94uqQyuw9k4d4D3eB11cuqo6lCIpC+9FhQoVYGxcXbEZ/P/T3FJfv8av+/fBZ5I/Wjm0gbVNY8yYMw/XI67ixrUIAICenj4+/3IgbBo3gZn5J2jdxhFffDkQEVfCP3DF4texU2c4OXeAhUUd1KljiXHjfVC5cmVc//+4r129ioGDh8DWzg41a9XC6DFjoaurh8hbt1QaN0mLyUOE7KwsRN6+hTaObRVlGhoaaNOmLa5fu6rCyNRPWXkvYh8+RLcuzujdvSumTvFDfNxTAEDk7VvIycmGQ5t/Hv1Zx7IuTM3McP16RL7nev7sGU4cD0Xzlq1KIvQCyc3NxeFDvyM9/Q3s7ZsBAOybNcPRI4eRnJQEuVyOw4d+R2ZWJlq2aq3iaN/isJU0VL7aKjIyEufPn4ejoyMaNWqEqKgoLFu2DJmZmRgyZAg6d+78weMzMzORmZmpVCZoFs9D6V8lvUJubi6MjIyUyo2MjBATc1/y69H7lYX3oomtHWbOnQeLOpZ48fw5fly7Cl8NH4Ld+w7i5YsXqFixInT19JSOqWZkjJcvXiiVfTd5Ik6fOoHMjAw4deiEqTPnlGQz8nXn72gMHTQAWVmZqFy5MpYsX4V69esDABYsWorJE33g3M4BFSpUgI6ODpYsW4naFhYqjvotdf+jLxWV9jyOHDmCpk2bYtKkSWjWrBmOHDkCZ2dn3L17Fw8fPoSrqytOnDjxwXMEBgZCX19faVvwQ2AJtYDo/do5OcPFtRsaNLSCY7v2WLZqHV6/fo3Qo+KeZOc7eQqCd/+CRctW4cnjWCxZML+YIi64OnUsseeXA9jx0x588eVATPvOH/fu3gUArFqxDK9fp2D9xi3YufsXDPUYgckTJ+DO39EqjpqkpNLkMXv2bPj5+eHly5fYvHkzBg0ahFGjRiE0NBTHjx+Hn58f5s//8H8oAQEBSE5OVtr8/AOKJV5DA0NoamrmmZB9+fIljI2Ni+WalL+y+F7o6unBwqIOHj+KhZGxMbKzs/E6JUWpTuLLFzD6T/zGxtVRx7IuOnTqjO+mzcLPe3bhxfNnJRl6HhW1tFDbwgI2jZtgvM9ENLRqhOAd2/AoNha7du7ArLnz4NDGEVaNGmHMWG/YNG6CXT8Ff/zEJYAT5tJQafK4desWhg8fDgDo378/Xr9+jc8//1yxf/Dgwbj+kaWX2tra0NPTU9qKY8gKePsfjLVNY1w4H6Yok8vluHAhDHb/P95LJaMsvhdv3qTh8aNHMDauDmubxqhQoSIuXjiv2P8gJgbxcXGws2v63nPI5W+fpZ6VlV3c4Yoil8uRnZWFjIx0AICGTPlPi4aGJgS5oIrQ8uCchzRUPufx7g3Q0NCAjo4O9PX1Fft0dXWRnJysqtDyNdRjBKZ954/GjZugia0ddmzfivT0dLj36avq0ArsTVoaYmNjFa+fPH6MqMhI6Ovrw8zcXIWRiVPa34ulC4Pg1LEjzMw+wfPnz7Bu9QpoaGrArXtPVNXVRe8+fbFk4Xzo6+ujStWqWBA4F3b2TWFr3xQAcPbP00h8+RI2jZugcuUquH/vDpYtXgj7Zs1h/sknKmvXsiWL0N7JGaZmZniTloZDv4fg8qWLWLN+I+pY1kXt2haYM2s6fCf5w8DAACdOHMP5sL+wYvU6lcVM0lNp8qhTpw7u3LmDevXqAQDCwsJQu3Ztxf7Y2FiYmZmpKrx8deveA68SE7F65XK8ePEcVo2ssXrdhjxDDaXZrVs38dWIYYrXC4PezhF91rsP5sxT/Xh6QZX29yLhWTy+95+E5KQkGBpWg33z5tiyYxcMq1UDAPhODoCGhgYm+45HVlYWHNu1g//30xXH62jr4MAve7F4wXxkZ2WhhqkpOnXpiuEjR6mqSQCAxMSXmBrgj+fPn6Gqri4aNrTCmvUb4di2HQBg5dr1WLZ4Eb71HoM3b96gdq3amDNvPpycO6g07nfUvMMgGZkgCCrrS65duxa1atVCz549893/3Xff4dmzZ9iwYYOo82bkSBEd0T+yc+WqDkESFTXL/up8nSJ+5G0x56Q0gQAIn9ZJsnOVNSpNHsWFyYOkxuRRejB5lA4qn/MgIipJHLaSBpMHEakVdV8lJZWy34clIqISx54HEakVdjykweRBRGqFw1bS4LAVERGJxp4HEakVdjykweRBRGqFw1bS4LAVERGJxp4HEakVdjykweRBRGqFw1bS4LAVERGJxp4HEakVdjykweRBRGqFw1bS4LAVEVEJOHPmDHr16gVzc3PIZDIcOHBAsS87Oxv+/v6wtbVFlSpVYG5ujmHDhuHp06dK56hTp06eR+HOn6/8ALfr16/DyckJOjo6qFWrFoKCgvLEsnfvXjRq1Ag6OjqwtbXFoUOHRLeHyYOI1IqqnmGelpYGe3t7rFq1Ks++N2/e4MqVK5g2bRquXLmCffv2ITo6Gp999lmeurNnz0ZcXJxiGzdunGJfSkoKXF1dYWFhgfDwcCxYsAAzZ87E+vXrFXXOnTuHgQMHwtPTE1evXoW7uzvc3d1x8+ZNUe3hw6CICoAPgyo9ivowqA5L/pImEACnfdoV6jiZTIb9+/fD3d39vXUuXbqE1q1b4+HDh4rHc9epUwcTJkzAhAkT8j1mzZo1+P777xEfHw8tLS0AwJQpU3DgwAFERUUBAL788kukpaUhJCREcVybNm3QtGlTrF27tsBtKPv/koiIVCQzMxMpKSlKW2ZmpiTnTk5Ohkwmg4GBgVL5/PnzYWRkhGbNmmHBggXIyfnn03JYWBicnZ0ViQMA3NzcEB0djVevXinquLi4KJ3Tzc0NYWFhouJj8iAitSLlsFVgYCD09fWVtsDAwCLHmJGRAX9/fwwcOBB6enqK8m+//Ra7du3CyZMn8fXXX2PevHmYPHmyYn98fDxq1KihdK53r+Pj4z9Y593+guJqKyJSK1IutgoICICvr69Smba2dpHOmZ2djf79+0MQBKxZs0Zp37+vZWdnBy0tLXz99dcIDAws8nXFYvIgIiokbW1tSf9ov0scDx8+xIkTJ5R6HflxcHBATk4OHjx4ACsrK5iamiIhIUGpzrvXpqamiv/Pr867/QXFYSsiUiuqWm31Me8Sx507d3Ds2DEYGRl99JiIiAhoaGjAxMQEAODo6IgzZ84gOztbUSc0NBRWVlYwNDRU1Dl+/LjSeUJDQ+Ho6CgqXvY8iEitqOo7gqmpqbh7967idUxMDCIiIlCtWjWYmZnh888/x5UrVxASEoLc3FzFHES1atWgpaWFsLAwXLhwAZ06dYKuri7CwsLg4+ODIUOGKBLDoEGDMGvWLHh6esLf3x83b97EsmXLsGTJEsV1x48fjw4dOmDRokXo2bMndu3ahcuXLyst5y0ILtUlKgAu1S09irpUt8sKcauKPuT4uIJ/Wj916hQ6deqUp9zDwwMzZ86EpaVlvsedPHkSHTt2xJUrVzB27FhERUUhMzMTlpaWGDp0KHx9fZWGzq5fvw4vLy9cunQJxsbGGDduHPz9/ZXOuXfvXkydOhUPHjxAgwYNEBQUhB49ehS4LQCTB1GBMHmUHkVNHl1XnpcmEACh3m0kO1dZw2ErIlIrvLWVNMr+xxAiIipx7HkQkVrhXXWlweRBRGpFg7lDEhy2IiIi0djzICK1wmEraTB5EJFaYe6QBpMHUQGUh+9HEEmJyYOI1IoM7HpIQZLkkZSUlOeBJUREpRFXW0lDdF/8hx9+wO7duxWv+/fvDyMjI3zyySe4du2apMEREVHpJDp5rF27FrVq1QLw9ja+oaGhOHz4MLp37w4/Pz/JAyQiklJpvSV7WSN62Co+Pl6RPEJCQtC/f3+4urqiTp06cHBwkDxAIiIpqfnffMmI7nkYGhri0aNHAIAjR44oHqQuCAJyc3OljY6IiEol0T2Pvn37YtCgQWjQoAFevnyJ7t27AwCuXr2K+vXrSx4gEZGUNNj1kITo5LFkyRLUqVMHjx49QlBQEKpWrQoAiIuLw9ixYyUPkIhISswd0uDDoIioTCnqw6D6bQqXJhAAv4xsIdm5ypoCvQ2//fZbgU/42WefFToYIqLipu6rpKRSoOTh7u5eoJPJZDJOmhNRqcbcIY0CJQ+5vHw8v5mIiKRRpNHDjIwM6OjoSBULEVGx42oraYj+nkdubi7mzJmDTz75BFWrVsX9+/cBANOmTcPGjRslD5CISEoyCTd1Jjp5/O9//8OWLVsQFBQELS0tRXmTJk2wYcMGSYMjIqLSSXTy2LZtG9avX4/BgwdDU1NTUW5vb4+oqChJgyMikhrvbSUN0XMeT548yfeb5HK5HNnZ2ZIERURUXHhLdmmI7nnY2Njgzz//zFP+888/o1mzZpIERUREpZvonsf06dPh4eGBJ0+eQC6XY9++fYiOjsa2bdsQEhJSHDESEUlG3YebpCK659G7d28cPHgQx44dQ5UqVTB9+nRERkbi4MGD6Nq1a3HESEQkGZlMuk2dFep7Hk5OTggNDZU6FiIiKiMK/SXBy5cvIzIyEsDbeZAWLdT3BmFEVHZw2EoaopPH48ePMXDgQPz1118wMDAAACQlJaFt27bYtWsXatasKXWMRESS4WoraYie8/jqq6+QnZ2NyMhIJCYmIjExEZGRkZDL5fjqq6+KI0YiIiplRPc8Tp8+jXPnzsHKykpRZmVlhRUrVsDJyUnS4IiIpMZhK2mITh61atXK98uAubm5MDc3lyQoIqLiwtQhDdHDVgsWLMC4ceNw+fJlRdnly5cxfvx4LFy4UNLgiIiodCrQY2gNDQ2VunppaWnIyclBhQpvOy7vfq5SpQoSExOLL9oC4mNoicqvoj6G9qvdN6UJBMCGL5tIdq6ypkBvw9KlS4s5DCKiksEpD2kUKHl4eHgUdxxERFSGiJ7z+LeMjAykpKQobUREpZmqbsl+5swZ9OrVC+bm5pDJZDhw4IDSfkEQMH36dJiZmaFSpUpwcXHBnTt3lOokJiZi8ODB0NPTg4GBATw9PZGamqpU5/r163BycoKOjg5q1aqFoKCgPLHs3bsXjRo1go6ODmxtbXHo0CFRbQEKkTzS0tLg7e0NExMTVKlSBYaGhkqbOti1Mxjdu3ZGq2a2GDzgC9y4fl3VIYlWHtoAlL12hF++hHFjx8ClY3vYN7bCiePHFPuys7OxZNEC9HPvBYeWTeHSsT2+D5iMZ88SVBhxwZWV90JV97ZKS0uDvb09Vq1ale/+oKAgLF++HGvXrsWFCxdQpUoVuLm5ISMjQ1Fn8ODBuHXrFkJDQxESEoIzZ85g9OjRiv0pKSlwdXWFhYUFwsPDsWDBAsycORPr169X1Dl37hwGDhwIT09PXL16Fe7u7nB3d8fNm+LmgkQnj8mTJ+PEiRNYs2YNtLW1sWHDBsyaNQvm5ubYtm2b2NOVOUcOH8LCoEB8PdYLu/buh5VVI3zztSdevnyp6tAKrDy0ASib7UhPfwMrKysETJ2RZ19GRgaiIm9j9JhvsHvvPixethIPYmIw3vsbFUQqTll8L0pa9+7dMXfuXPTp0yfPPkEQsHTpUkydOhW9e/eGnZ0dtm3bhqdPnyp6KJGRkThy5Ag2bNgABwcHtG/fHitWrMCuXbvw9OlTAEBwcDCysrKwadMmNG7cGAMGDMC3336LxYsXK661bNkydOvWDX5+frC2tsacOXPQvHlzrFy5UlR7RCePgwcPYvXq1ejXrx8qVKgAJycnTJ06FfPmzUNwcLDY05U527duRt/P+8O9Tz/Uq18fU2fMgo6ODg7s+0XVoRVYeWgDUDbb0d6pA7zH+6CLS947UOvq6mLdhs1w69YDdSzrws6+KQK+n4bbt24h7v//OJRWZem90JDJJNsyMzPzDN1nZmaKjikmJgbx8fFwcXFRlOnr68PBwQFhYWEAgLCwMBgYGKBly5aKOi4uLtDQ0MCFCxcUdZydnZUeEe7m5obo6Gi8evVKUeff13lX5911Ckp08khMTETdunUBAHp6eoqlue3bt8eZM2fEnq5Myc7KQuTtW2jj2FZRpqGhgTZt2uL6tasqjKzgykMbgPLTjo9JTU2FTCaDrp6eqkN5r7L2Xkg5bBUYGAh9fX2lLTAwUHRM8fHxAIAaNWooldeoUUOxLz4+HiYmJkr7K1SogGrVqinVye8c/77G++q8219QopNH3bp1ERMTAwBo1KgR9uzZA+Btj+TdjRLLq1dJr5CbmwsjIyOlciMjI7x48UJFUYlTHtoAlJ92fEhmZiaWLl6I7j16omrVqqoO573U4b14n4CAACQnJyttAQEBqg6rRIhOHiNGjMC1a9cAAFOmTMGqVaugo6MDHx8f+Pn5iQ4gPT0dZ8+exe3bt/Psy8jI+Og8ilTdRqLSJDs7G36+4yEIAr6fPkvV4ZQrUq620tbWhp6entKmra0tOiZTU1MAQEKC8uKIhIQExT5TU1M8e/ZMaX9OTg4SExOV6uR3jn9f43113u0vKNHJw8fHB99++y2At+NtUVFR2LlzJ65evYrx48eLOtfff/8Na2trODs7w9bWFh06dEBcXJxif3JyMkaMGPHBc+TXbVzwg/huY0EYGhhCU1MzzyTgy5cvYWxsXCzXlFp5aANQftqRn+zsbPhNnIC4p0+xbsOmUt3rAMree6Eh4SYVS0tLmJqa4vjx44qylJQUXLhwAY6OjgAAR0dHJCUlITw8XFHnxIkTkMvlcHBwUNQ5c+aM0v0HQ0NDYWVlpVgN6+joqHSdd3XeXaegitx+CwsL9O3bF3Z2dqKP9ff3R5MmTfDs2TNER0dDV1cX7dq1Q2xsbIHPkV+30c+/eLqNFbW0YG3TGBfO/zOxJJfLceFCGOzsmxXLNaVWHtoAlJ92/Ne7xBH78CHWbdwCA4PSv/y9vL4XUktNTUVERAQiIiIAvJ0kj4iIQGxsLGQyGSZMmIC5c+fit99+w40bNzBs2DCYm5vD3d0dAGBtbY1u3bph1KhRuHjxIv766y94e3tjwIABipvSDho0CFpaWvD09MStW7ewe/duLFu2DL6+voo4xo8fjyNHjmDRokWIiorCzJkzcfnyZXh7e4tqT4G+Yb58+fICn/Bdr6Qgzp07h2PHjsHY2BjGxsY4ePAgxo4dCycnJ5w8eRJVqlT56Dm0tbXzdBOL895WQz1GYNp3/mjcuAma2Nphx/atSE9Ph3ufvsV3UYmVhzYAZbMdb9LSlD4cPXn8GFGRkdDX14dx9eqY5PMtIiNvY8WqdZDn5uLF8+cA3q68qfivFTSlTVl6L1R1S/bLly+jU6dOitfv/qB7eHhgy5YtmDx5MtLS0jB69GgkJSWhffv2OHLkCHR0dBTHBAcHw9vbG126dIGGhgb69eun9PdZX18ff/zxB7y8vNCiRQsYGxtj+vTpSt8Fadu2LXbu3ImpU6fiu+++Q4MGDXDgwAE0aSLuPl0FujGipaVlwU4mk+H+/fsFvrienh4uXLgAa2trpXJvb2/8+uuv2LlzJzp27Ijc3NwCnxMo/hsj/hS8A1s3b8SLF89h1cga/t9NhZ2dffFeVGLloQ1A2WvHpYsX8NWIYXnKP+vdB2O8vNHDtUu+x23YvA2tWjsUd3hFUlLvRVFvjDjh1yhpAgGwtHcjyc5V1hQoeRSX1q1bY9y4cRg6dGiefd7e3ggODkZKSkqpSx5EpDpMHqWDlHM+ovXp0wc//fRTvvtWrlyJgQMHQoW5jYjKIQ2ZdJs6U2nPo7iw50FUfhW15zHxYLQ0gQBY1Mvq45XKKZX2PIiIqGwqYg4nIipb1H24SSpMHkSkVvgkQWkUatjqzz//xJAhQ+Do6IgnT54AALZv346zZ89KGhwREZVOopPHL7/8Ajc3N1SqVAlXr15V3EcqOTkZ8+bNkzxAIiIpSXlLdnUmOnnMnTsXa9euxY8//oiKFSsqytu1a4crV65IGhwRkdRK472tyiLR7Y+Ojoazs3Oecn19fSQlJUkRExERlXKik4epqSnu3r2bp/zs2bOKh0QREZVWqnqGeXkjOnmMGjUK48ePx4ULFyCTyfD06VMEBwdj0qRJ+Oab0v+sZSJSb5zzkIbopbpTpkyBXC5Hly5d8ObNGzg7O0NbWxuTJk3CuHHjiiNGIiIqZQp9e5KsrCzcvXsXqampsLGxKVUPrOHtSYjKr6LenmT60TvSBAJgtlsDyc5V1hT6bdDS0oKNjY2UsRARFTt+w1waopNHp06dPvgwlRMnThQpICIiKv1EJ4+mTZsqvc7OzkZERARu3rwJDw8PqeIiIioW6j7RLRXRyWPJkiX5ls+cOROpqalFDoiIqDgxd0hDsi9JDhkyBJs2bZLqdEREVIpJdlfdsLAwpQe1ExGVRpwwl4bo5NG3b1+l14IgIC4uDpcvX8a0adMkC4yIqDjIwOwhBdHJQ19fX+m1hoYGrKysMHv2bLi6ukoWGBERlV6ikkdubi5GjBgBW1tbGBoaFldMRETFhsNW0hA1Ya6pqQlXV1fePZeIyiwNmXSbOhO92qpJkya4f/9+ccRCRERlRKEeBjVp0iSEhIQgLi4OKSkpShsRUWkmk8kk29RZgec8Zs+ejYkTJ6JHjx4AgM8++0zplycIAmQyGXJzc6WPkohIIuo+3CSVAt9VV1NTE3FxcYiMjPxgvQ4dOkgSWFHwrrpE5VdR76q76LR0w+4TO6jvA/AK/Da8yzGlITkQERWWmo82SUZUDlf3MT4iKvt4Y0RpiEoeDRs2/GgCSUxMLFJARERU+olKHrNmzcrzDXMiorKEE+bSEJU8BgwYABMTk+KKhYio2HHUShoF/p4H5zuIiOgd0autiIjKMg3eVVcSBU4ecrm8OOOgfBi28lZ1CEX26tJKVYdApISDKNKQ7EmCRESkPiR7kiARUVnA1VbSYM+DiNSKhkwm2SZGnTp18r25opeXFwCgY8eOefaNGTNG6RyxsbHo2bMnKleuDBMTE/j5+SEnR/l+TKdOnULz5s2hra2N+vXrY8uWLUX6fb0Pex5ERCXg0qVLSjeOvXnzJrp27YovvvhCUTZq1CjMnj1b8bpy5cqKn3Nzc9GzZ0+Ympri3LlziIuLw7Bhw1CxYkXMmzcPABATE4OePXtizJgxCA4OxvHjx/HVV1/BzMwMbm5ukraHyYOI1IqqJsyrV6+u9Hr+/PmoV6+e0v0CK1euDFNT03yP/+OPP3D79m0cO3YMNWrUQNOmTTFnzhz4+/tj5syZ0NLSwtq1a2FpaYlFixYBAKytrXH27FksWbJE8uTBYSsiUitSDltlZmbmeaZRZmbmR2PIysrCjh07MHLkSKXv0AUHB8PY2BhNmjRBQEAA3rx5o9gXFhYGW1tb1KhRQ1Hm5uaGlJQU3Lp1S1HHxcVF6Vpubm4ICwsr6q8tDyYPIqJCCgwMhL6+vtIWGBj40eMOHDiApKQkDB8+XFE2aNAg7NixAydPnkRAQAC2b9+OIUOGKPbHx8crJQ4Aitfx8fEfrJOSkoL09PTCNjNfHLYiIrUi5bBVQEAAfH19lcq0tbU/etzGjRvRvXt3mJubK8pGjx6t+NnW1hZmZmbo0qUL7t27h3r16kkXtESYPIhIrUg53KKtrV2gZPFvDx8+xLFjx7Bv374P1nNwcAAA3L17F/Xq1YOpqSkuXryoVCchIQEAFPMkpqamirJ/19HT00OlSpVExfkxHLYiIipBmzdvhomJCXr27PnBehEREQAAMzMzAICjoyNu3LiBZ8+eKeqEhoZCT08PNjY2ijrHjx9XOk9oaCgcHR0lbMFbTB5EpFby+65FYTex5HI5Nm/eDA8PD1So8M/Az7179zBnzhyEh4fjwYMH+O233zBs2DA4OzvDzs4OAODq6gobGxsMHToU165dw9GjRzF16lR4eXkpej9jxozB/fv3MXnyZERFRWH16tXYs2cPfHx8pPnl/QuTBxGpFZmEm1jHjh1DbGwsRo4cqVSupaWFY8eOwdXVFY0aNcLEiRPRr18/HDx4UFFHU1MTISEh0NTUhKOjI4YMGYJhw4YpfS/E0tISv//+O0JDQ2Fvb49FixZhw4YNki/TBQCZUA5vl5uR8/E6ZQFvjEiUl04RZ2q3XX4kTSAAhrWsJdm5yhpOmBORWuEzzKXB5EFEaoWpQxqc8yAiItHY8yAitcJRK2kweRCRWinMElvKi8NWREQkGnseRKRW+IlZGkweRKRWOGwlDSZhIiISjT0PIlIr7HdIg8mDiNQKh62kwWErIiISjT0PIlIr/MQsDSYPIlIrHLaSBpMwERGJxp4HEakV9jukweRBRGqFo1bS4LAVERGJxp4HEakVDQ5cSYLJQ4TuXTvj6dMnecq/HDAI302bUezXb9e8HnyGuaC5TW2YVddHf5/1OHjqumK/STVdzB3fGy6O1tCvWglnr9yFb9Be3It9rnQeBztLzPT6FK1s6yA3V47rfz9Br7GrkJGZDacWDfDHhvH5Xr/94CCE345VKqtbyxjnf5qCXLkcZs6TpW/0R+zaGYytmzfixYvnaGjVCFO+mwZbO7sSj6Ow1qxagbWrlZ/zXsfSEr+GHFFRRB8XfvkStmzaiMjbN/H8+XMsWb4Knbu4KPavWbUCRw7/jvj4eFSsWBE2No3hPd4Hdnb2Koz6Hxy2kgaThwjBu3+GPDdX8fru3Tv4+qsR6OrWrUSuX6WSNm78/QTbfg3D7sWj8+zfs2Q0snNy8cWEdUhJy8C3Qzrj0NpxaNZ3Lt5kZAF4mzh+XTkWCzf/Ad8f9iInVw67hp9ALhcAAOev3UcdlwCl804f+yk6tbbKkzgqVNDAtsAR+OvqPbSxtyymVr/fkcOHsDAoEFNnzIKtrT2Ct2/FN1974teQIzAyMirxeAqrXv0GWL9hs+K1ZgVNFUbzcenpb2BlZQX3vv3gO947z34LizoI+H46atashYzMDOzYtgXfjBqJg4dDUa1aNRVETMWByUOE//7D37RhPWrVqo2WrVqXyPX/+Os2/vjrdr776tc2gYOdJZr3m4vI+/EAgG/n7caDY/PQv3sLbNkfBgAImtgXq3edwsLNoYpj7zx8pvg5OycXCS9fK15XqKCBTzvaYc2u03muOXNsL0THJODkxWiVJI/tWzej7+f94d6nHwBg6oxZOHPmFA7s+wWeo/Im19KqgqYmjKtXV3UYBdbeqQPaO3V47/4en/ZSej1pcgD2//Iz7vwdDYc2jsUd3kfJOGwlCU6YF1J2VhZ+D/kN7n37lYovHWlrvf0ckJGVoygTBAFZWTlo27QeAKC6YVW0trPE88RUnNziiwfH5uGPDePRtmnd95730w52MNKvgu2/nlcq79CqIfp2bYYJ8/cUQ2s+LjsrC5G3b6GNY1tFmYaGBtq0aYvr166qJKbCehj7EC4d26OHWxcETJ6IuKdPVR2SZLKzsvDL3t3Q1dVFQysrVYcD4O2wlVSbOivVyePRo0cYOXLkB+tkZmYiJSVFacvMzCz22E6cOIbXr1/jM/c+xX6tgoh+EI/YuETMGfcZDHQroWIFTUwc7oKapoYwNdYHAFjWNAYAfP91D2zadw69vVYjIvIRDq0bh3q18//k6+HuiNCwSDx5lqQoq6ZfBT/OGoJRM7bjdVpGsbctP6+SXiE3NzfP8JSRkRFevHihkpgKw9bODnP+F4jV6zbg+2kz8eTJE4wYNhhpaamqDq1ITp86iTYtm6FVczts37YFa3/cBENDDlmVJ6U6eSQmJmLr1q0frBMYGAh9fX2lbcEPgcUe2/5ffkG79s4wMalR7NcqiJwcOQZM/BH1LUwQd2YBEsMWw7llQxw5ewtyQQ4A0NB4+1Fp4y9nsf2387gW/RiTF+3D3w+ewaN33uGET0wM0NXRGlsPhCmVr542ELuPXMZfV+4Vf8PKufZOHeDq1h0NrRqhXXsnrFyzHq9fp+DokcOqDq1IWrV2wJ5fDmBb8C60a+8Ev4kT8PLlS1WHBeDtaiupNnWm0jmP33777YP779+//9FzBAQEwNfXV6lM0NQuUlwf8/TpE1w4fw6Ll60o1uuIdTXyEdoMmA+9qjrQqlgBL16l4sy2SYqJ7rjnKQCgmBN5JzomHrVMDfOcb2jvNniZnIaQ09eVyju0boieHWwxYWgXAG/vFaSpqYHXl5bBa+5P2PafIa7iYGhgCE1NzTx/kF6+fAljY+Niv35x0dPTg4VFHTyKjf145VKscuXKqG1hgdoWFrCzb4pe3V1xYN/P8Bz1tapDU/vhJqmoNHm4u7tDJpNBEIT31vnYfIK2tja0tZWTRUbOeypL5Nf9+1CtmhGcnDsW74UKKSX17VBSvdrV0dymNmatDgEAPHz6Ek+fJaFhHROl+vUtTPKdiB/2WRvsDLmInBy5UnlHj0XQ1Pin0/ppRztMHO6CTsMX4+m/hreKU0UtLVjbNMaF82GKZaJyuRwXLoRhwMAhJRJDcXiTloZHjx6h52dlZwK9IOSCHFlZWaoOgySk0uRhZmaG1atXo3fv3vnuj4iIQIsWLUo4qg+Ty+X4df8+9OrtjgoVSvbXV6WSFurV+uePSp1PjGDX8BO8SnmDR/Gv0NelGZ6/SsWj+EQ0aWCOhX6f4+Cp6zh+PkpxzJKtxzB1TE/c+PsJrkU/xpBeDrCqUwOD/DYqXatj64awrGmMzfvP5YkjOiZB6XVzm9qQCwJu34uTuMUfNtRjBKZ954/GjZugia0ddmzfivT0dLj36VuicRTFogU/oEPHTjAzN8fzZ8+wZtUKaGpqoHuPT1Ud2nu9SUtD7L96Rk8eP0ZUZOTbYWMDA2xYvxYdO3WGcfXqSHr1Crt+CsazhIQSW9L+Mex5SEOlyaNFixYIDw9/b/L4WK9EFc6HnUNc3FO49+1X4tdubmOh9AW+oElvY9j+23mMnrEDptX18MPEvjAx0kX8ixQEh1xA4HrlL5ut3HkKOtoVETSxHwz1K+PG30/w6TcrEfNYeZJ5uHtbhEXcw98PlBNFadKtew+8SkzE6pXL8eLFc1g1ssbqdRtgVIaGrRIS4jHFzxdJSUkwrFYNzZq3wPade0r19yFu3bqJr0YMU7xeGPR2jvGz3n0wdcYsxMTcx2+/7kfSq1cwMDBA4ya22LwtGPXrN1BVyEq4VFcaMkGFf53//PNPpKWloVu3/D+RpKWl4fLly+jQ4f1ryvNT3MNWJcWwVd4vYJU1ry6t/HglIhF0iviRNzRSutV4Xa3LzgcVqam05+Hk5PTB/VWqVBGdOIiIPkSDHQ9J8BvmRKRWOGwljVL9PQ8iIiqd2PMgIrXC1VbSYPIgIrXCYStpcNiKiIhEY8+DiNQKV1tJg8mDiNQKh62kwWErIqISMHPmTMhkMqWtUaNGiv0ZGRnw8vKCkZERqlatin79+iEhQfkOD7GxsejZsycqV64MExMT+Pn5ISdH+VvRp06dQvPmzaGtrY369etjy5YtxdIeJg8iUiuqfBhU48aNERcXp9jOnj2r2Ofj44ODBw9i7969OH36NJ4+fYq+ff+5T1tubi569uyJrKwsnDt3Dlu3bsWWLVswffp0RZ2YmBj07NkTnTp1QkREBCZMmICvvvoKR48eLdLvLD8ctiIitaLKQasKFSrA1NQ0T3lycjI2btyInTt3onPnzgCAzZs3w9raGufPn0ebNm3wxx9/4Pbt2zh27Bhq1KiBpk2bYs6cOfD398fMmTOhpaWFtWvXwtLSEosWLQIAWFtb4+zZs1iyZAnc3NwkbQt7HkREhST2SaZ37tyBubk56tati8GDByvuThweHo7s7Gy4uLgo6jZq1Ai1a9dGWNjbh7GFhYXB1tYWNWr88wA6Nzc3pKSk4NatW4o6/z7HuzrvziElJg8iUisaMplkW35PMg0MzP9Jpg4ODtiyZQuOHDmCNWvWICYmBk5OTnj9+jXi4+OhpaUFAwMDpWNq1KiB+Pi3D2+Lj49XShzv9r/b96E6KSkpSE9Pl+LXp8BhKyJSK1IOW+X3JNP/Ppzune7duyt+trOzg4ODAywsLLBnzx5UqlRJwqhKBnseRESFpK2tDT09PaXtfcnjvwwMDNCwYUPcvXsXpqamyMrKQlJSklKdhIQExRyJqalpntVX715/rI6enp7kCYrJg4jUi0zCrQhSU1Nx7949mJmZoUWLFqhYsSKOHz+u2B8dHY3Y2Fg4OjoCABwdHXHjxg08e/ZMUSc0NBR6enqwsbFR1Pn3Od7VeXcOKTF5EJFakUn4PzEmTZqE06dP48GDBzh37hz69OkDTU1NDBw4EPr6+vD09ISvry9OnjyJ8PBwjBgxAo6OjmjTpg0AwNXVFTY2Nhg6dCiuXbuGo0ePYurUqfDy8lL0dsaMGYP79+9j8uTJiIqKwurVq7Fnzx74+PhI/nvknAcRUQl4/PgxBg4ciJcvX6J69epo3749zp8/j+rVqwMAlixZAg0NDfTr1w+ZmZlwc3PD6tWrFcdramoiJCQE33zzDRwdHVGlShV4eHhg9uzZijqWlpb4/fff4ePjg2XLlqFmzZrYsGGD5Mt0ARU/hra48DG0pQcfQ0tSK+pjaC/eT5YmEACt6+pLdq6yhj0PIlIrvLOVNDjnQUREorHnQUTqhV0PSTB5EJFa4S3ZpcFhKyIiEo09DyJSK4W5lTrlxZ4HERGJxp4HEakVdjykweRBROqF2UMSHLYiIiLR2PMgIrXCpbrSYPIgIrXC1VbS4LAVERGJxp4HEakVdjykUS6TR3m5yTxvZ05UDJg9JMFhKyIiEq1c9jyIiN6Hq62kweRBRGqFq62kwWErIiISjT0PIlIr7HhIg8mDiNQLs4ckOGxFRESisedBRGqFq62kweRBRGqFq62kwWErIiISjT0PIlIr7HhIg8mDiNQLs4ckOGxFRESisedBRGqFq62kweRBRGqFq62kwWErIiISjT0PIlIr7HhIg8mDiNQLs4ckOGxFRESisedBRGqFq62kweRBRGqFq62kwWErIiISjT0PIlIr7HhIgz0PIlIvMgk3EQIDA9GqVSvo6urCxMQE7u7uiI6OVqrTsWNHyGQypW3MmDFKdWJjY9GzZ09UrlwZJiYm8PPzQ05OjlKdU6dOoXnz5tDW1kb9+vWxZcsWccEWAJMHEVEJOH36NLy8vHD+/HmEhoYiOzsbrq6uSEtLU6o3atQoxMXFKbagoCDFvtzcXPTs2RNZWVk4d+4ctm7dii1btmD69OmKOjExMejZsyc6deqEiIgITJgwAV999RWOHj0qaXtkgiAIkp6xFEjPVnUE0uDEHlFeOkUcbL//PEOaQADUra5T6GOfP38OExMTnD59Gs7OzgDe9jyaNm2KpUuX5nvM4cOH8emnn+Lp06eoUaMGAGDt2rXw9/fH8+fPoaWlBX9/f/z++++4efOm4rgBAwYgKSkJR44cKXS8/8WeBxGpFZlMui0zMxMpKSlKW2ZmZoHiSE5OBgBUq1ZNqTw4OBjGxsZo0qQJAgIC8ObNG8W+sLAw2NraKhIHALi5uSElJQW3bt1S1HFxcVE6p5ubG8LCwgr1+3ofJg8iokIKDAyEvr6+0hYYGPjR4+RyOSZMmIB27dqhSZMmivJBgwZhx44dOHnyJAICArB9+3YMGTJEsT8+Pl4pcQBQvI6Pj/9gnZSUFKSnpxe6rf/F1VZEpFakHA0OCAiAr6+vUpm2tvZHj/Py8sLNmzdx9uxZpfLRo0crfra1tYWZmRm6dOmCe/fuoV69etIELRH2PIhIvUi42kpbWxt6enpK28eSh7e3N0JCQnDy5EnUrFnzg3UdHBwAAHfv3gUAmJqaIiEhQanOu9empqYfrKOnp4dKlSp98HpiMHkQEZUAQRDg7e2N/fv348SJE7C0tPzoMREREQAAMzMzAICjoyNu3LiBZ8+eKeqEhoZCT08PNjY2ijrHjx9XOk9oaCgcHR0laslbTB7/L/zyJXzrNQZdO7VH0yZWOHH8mNL+ad9PQdMmVkrb2K89leokJychwH8i2jk0R3vHlpg57Tu8eaO8DK+khV++hHFjx8ClY3vYN87brjdpaZg3dza6dnZG6+Z26NOrB/bs/klF0Yq3a2cwunftjFbNbDF4wBe4cf26qkMSray3Yc+unfi8Ty+0bd0cbVs3x9BBX+Lsn6dVHdZ7yST8nxheXl7YsWMHdu7cCV1dXcTHxyM+Pl4xD3Hv3j3MmTMH4eHhePDgAX777TcMGzYMzs7OsLOzAwC4urrCxsYGQ4cOxbVr13D06FFMnToVXl5eih7PmDFjcP/+fUyePBlRUVFYvXo19uzZAx8fH0l/j0we/y89/Q0aWlkh4PsZ763Trr0Tjp06q9jmBy1W2v+d/yTcu3sXa3/cjBWr1iI8/DJmz5z+nrOVjPT0N7CyskLA1PzbtTBoPs6d/RPz5i/A/oOHMHioB+b/bw5OnTieb/3S5MjhQ1gYFIivx3ph1979sLJqhG++9sTLly9VHVqBlYc2mNQwxXifSfhp7z7s3PMLWju0wXhvL9y9e0fVoeVLytVWYqxZswbJycno2LEjzMzMFNvu3bsBAFpaWjh27BhcXV3RqFEjTJw4Ef369cPBgwcV59DU1ERISAg0NTXh6OiIIUOGYNiwYZg9e7aijqWlJX7//XeEhobC3t4eixYtwoYNG+Dm5ibJ7+8dfs8jH02bWGHxslXo3OWf5W7Tvp+C169TsHT56nyPuX/vHvr27oHgXT+jcRNbAMBfZ8/A+5vROHr8NExMauR73IdI/T0P+8ZWWLJcuV19e38Kt27d8fU3XoqyAV/0Rfv2TvAeL+0nFakNHvAFGjexxXdT3yZouVwO1y4dMHDQUHiOGv2Ro0uH8tCG/Dg5tobPJD/07feF5Ocu6vc8YhMLtpS2IGpX+/jkeHnFnocIly9dRCdnR/T+1A3/mz0DSUmvFPuuX7sKXT09ReIAAIc2baGhoYGbpXgYomnTZjh98gQSEhIgCAIuXjiPhw9i4NiuvapD+6DsrCxE3r6FNo5tFWUaGhpo06Ytrl+7qsLICq48tOG/cnNzcfjQ70hPfwN7+2aqDidfKro7Sbmj0qW6L168wKZNmxAWFqZYo2xqaoq2bdti+PDhqF69uirDU9KunRO6uHTFJ5/UxKNHj7By2WJ4jRmFbcG7oampiRcvXuT5sk+FChWgp6+PFy+eqyjqj5vy/TTMnjENrp2dUaFCBchkMsyYNRctWrZSdWgf9CrpFXJzc2FkZKRUbmRkhJiY+yqKSpzy0IZ37vwdjaGDBiArKxOVK1fGkuWrUK9+fVWHlS/euUEaKksely5dgpubGypXrgwXFxc0bNgQwNslZcuXL8f8+fNx9OhRtGzZ8oPnyczMzPONTrmGdoHWWovRrUdPxc8NGlqhYUMrfNrdBZcvXYRDG2lXMZSkn4K34/r1CCxbuQbm5uYIv3wZ8+bOQnUTE6VPxEQfUqeOJfb8cgCpqa8R+sdRTPvOHxu37Ci1CYSKTmXJY9y4cfjiiy+wdu1ayP7zUUAQBIwZMwbjxo376FfqAwMDMWvWLKWy76bOwNTpM6UOWUnNWrVgaGiIR7EP4dDGEcbGxkhMTFSqk5OTg5TkZBgbl54e1L9lZGRg+dIlWLJ8JZw7dAQANLRqhOjoSGzdvLFUJw9DA0NoamrmmVh++fIljI2NVRSVOOWhDe9U1NJCbQsLAIBN4ya4dfMGgndsw/SZsz9ypCqw6yEFlc15XLt2DT4+PnkSBwDIZDL4+Pgo1jh/SEBAAJKTk5U2P/+AYohYWUJ8PJKSkmD8/0NrdvbN8DolBbdv/XMzsosXzkMul6PJ/y+zK21ycnKQk5MNDQ3l90BDQxPyUr6OoqKWFqxtGuPC+X8+XMjlcly4EAa7UjrW/l/loQ3vI5fLkZ2Vpeow8qWq1Vbljcp6Hqamprh48SIaNWqU7/6LFy/muT9LfrS18w5RFWa11Zs3aYiNjVW8fvLkMaKiIhX3q1m7eiVcurrByNgYjx89wtLFC1CrtgXatnMCANStVw/t2jth9sxp+H76LORkZ2P+vDlw696zUCutpPIm7T/tevwYUZFv22Vmbo6WrVpj8cIF0NbWgZm5OcIvXULIbwcwafIUlcVcUEM9RmDad/5o3LgJmtjaYcf2rUhPT4d7n76qDq3AykMbli1ZhPZOzjA1M8ObtDQc+j0Ely9dxJr1G1UdGhUjlS3VXbVqFSZOnIivv/4aXbp0USSKhIQEHD9+HD/++CMWLlyIsWPHij53YZLHpYsXMGrksDzlvXr3wffTZsLnWy9ERd3G65TXqG5iAse27eDlPR5G/xpeSE5OQuD/5uDMqRPQ0NBAFxdX+H83FZUrVxEfEKT5ZHPp4gV8NSJvuz7r3Qdz5s3Hi+fPsWzpYoSdO4uU5GSYmZuj3+dfYqjH8Hx7haXNT8E7sHXzRrx48RxWjazh/91U2NnZqzosUcp6G2ZM+w4Xz5/H8+fPUFVXFw0bWmGE5yg4tm1XLNcr6lLdp0nS9YjMDbQkO1dZo9LveezevRtLlixBeHg4cnNzAbz9EkyLFi3g6+uL/v37F+q8fJ4HUflV1OQRlyxd8jDTZ/JQqezsbLx48QIAYGxsjIoVKxbpfEweROUXk0fpUCpuyV6xYkXFjb+IiIqT2HtSUf5KRfIgIioxzB2S4O1JiIhINPY8iEitsOMhDSYPIlIrXIgiDQ5bERGRaOx5EJFa4WoraTB5EJF6Ye6QBIetiIhINPY8iEitsOMhDSYPIlIrXG0lDQ5bERGRaOx5EJFa4WoraTB5EJFa4bCVNDhsRUREojF5EBGRaBy2IiK1wmErabDnQUREorHnQURqhautpMHkQURqhcNW0uCwFRERicaeBxGpFXY8pMHkQUTqhdlDEhy2IiIi0djzICK1wtVW0mDyICK1wtVW0uCwFRERicaeBxGpFXY8pMGeBxGpF5mEWyGsWrUKderUgY6ODhwcHHDx4sWitEZlmDyIiErI7t274evrixkzZuDKlSuwt7eHm5sbnj17purQRJMJgiCoOgippWerOgJpcGKPKC+dIg62S/n3oVJFcfUdHBzQqlUrrFy5EgAgl8tRq1YtjBs3DlOmTJEusBLAngcRqRWZTLpNjKysLISHh8PFxUVRpqGhARcXF4SFhUncyuLHCXMiokLKzMxEZmamUpm2tja0tbXz1H3x4gVyc3NRo0YNpfIaNWogKiqqWOMsFgKJlpGRIcyYMUPIyMhQdShFUh7aUR7aIAjlox3loQ1izZgxQwCgtM2YMSPfuk+ePBEACOfOnVMq9/PzE1q3bl0C0UqrXM55FLeUlBTo6+sjOTkZenp6qg6n0MpDO8pDG4Dy0Y7y0AaxxPQ8srKyULlyZfz8889wd3dXlHt4eCApKQm//vprcYcrKc55EBEVkra2NvT09JS2/BIHAGhpaaFFixY4fvy4okwul+P48eNwdHQsqZAlwzkPIqIS4uvrCw8PD7Rs2RKtW7fG0qVLkZaWhhEjRqg6NNGYPIiISsiXX36J58+fY/r06YiPj0fTpk1x5MiRPJPoZQGTRyFoa2tjxowZ7+2elhXloR3loQ1A+WhHeWhDSfD29oa3t7eqwygyTpgTEZFonDAnIiLRmDyIiEg0Jg8iIhKNyYOIiERj8iiEsn4//jNnzqBXr14wNzeHTCbDgQMHVB2SaIGBgWjVqhV0dXVhYmICd3d3REdHqzos0dasWQM7OzvFF8wcHR1x+PBhVYdVJPPnz4dMJsOECRNUHQoVIyYPkcrD/fjT0tJgb2+PVatWqTqUQjt9+jS8vLxw/vx5hIaGIjs7G66urkhLS1N1aKLUrFkT8+fPR3h4OC5fvozOnTujd+/euHXrlqpDK5RLly5h3bp1sLOzU3UoVNxUe2utsqd169aCl5eX4nVubq5gbm4uBAYGqjCqwgMg7N+/X9VhFNmzZ88EAMLp06dVHUqRGRoaChs2bFB1GKK9fv1aaNCggRAaGip06NBBGD9+vKpDomLEnocI5e1+/OVJcnIyAKBatWoqjqTwcnNzsWvXLqSlpZXJex15eXmhZ8+eSv99UPnFb5iLUO7ux19OyOVyTJgwAe3atUOTJk1UHY5oN27cgKOjIzIyMlC1alXs378fNjY2qg5LlF27duHKlSu4dOmSqkOhEsLkQWWel5cXbt68ibNnz6o6lEKxsrJCREQEkpOT8fPPP8PDwwOnT58uMwnk0aNHGD9+PEJDQ6Gjo6PqcKiEMHmIYGxsDE1NTSQkJCiVJyQkwNTUVEVRqTdvb2+EhITgzJkzqFmzpqrDKRQtLS3Ur18fANCiRQtcunQJy5Ytw7p161QcWcGEh4fj2bNnaN68uaIsNzcXZ86cwcqVK5GZmQlNTU0VRkjFgXMeIpS3+/GXZYIgwNvbG/v378eJEydgaWmp6pAkI5fL8zxgqDTr0qULbty4gYiICMXWsmVLDB48GBEREUwc5RR7HiKVh/vxp6am4u7du4rXMTExiIiIQLVq1VC7dm0VRlZwXl5e2LlzJ3799Vfo6uoiPj4eAKCvr49KlSqpOLqCCwgIQPfu3VG7dm28fv0aO3fuxKlTp3D06FFVh1Zgurq6eeaaqlSpAiMjozI5B0UFpOrlXmXRihUrhNq1awtaWlpC69athfPnz6s6JFFOnjyZ57nLAAQPDw9Vh1Zg+cUPQNi8ebOqQxNl5MiRgoWFhaClpSVUr15d6NKli/DHH3+oOqwi41Ld8o+3ZCciItE450FERKIxeRARkWhMHkREJBqTBxERicbkQUREojF5EBGRaEweREQkGpMHFYvhw4fD3d1d8bpjx44qebLcqVOnIJPJkJSU9N46Yp+mOHPmTDRt2rRIcT148AAymQwRERFFOg+RqjB5qJHhw4dDJpNBJpMpbsY3e/Zs5OTkFPu19+3bhzlz5hSobkH+4BORavHeVmqmW7du2Lx5MzIzM3Ho0CF4eXmhYsWKCAgIyFM3KysLWlpakly3LD+kiYjyYs9DzWhra8PU1BQWFhb45ptv4OLigt9++w3AP0NN//vf/2Bubg4rKysAb5/X0L9/fxgYGKBatWro3bs3Hjx4oDhnbm4ufH19YWBgACMjI0yePBn/vevNf4etMjMz4e/vj1q1akFbWxv169fHxo0b8eDBA3Tq1AkAYGhoCJlMhuHDhwN4e7fZwMBAWFpaolKlSrC3t8fPP/+sdJ1Dhw6hYcOGqFSpEjp16qQUZ0H5+/ujYcOGqFy5MurWrYtp06YhOzs7T71169ahVq1aqFy5Mvr37694muE7GzZsgLW1NXR0dNCoUSOsXr36vdd89eoVBg8ejOrVq6NSpUpo0KABNm/eLDp2opLCnoeaq1SpEl6+fKl4ffz4cejp6SE0NBQAkJ2dDTc3Nzg6OuLPP/9EhQoVMHfuXHTr1g3Xr1+HlpYWFi1ahC1btmDTpk2wtrbGokWLsH//fnTu3Pm91x02bBjCwsKwfPly2NvbIyYmBi9evECtWrXwyy+/oF+/foiOjoaenp7iLrmBgYHYsWMH1q5diwYNGuDMmTMYMmQIqlevjg4dOuDRo0fo27cvvLy8MHr0aFy+fBkTJ04U/TvR1dXFli1bYG5ujhs3bmDUqFHQ1dXF5MmTFXXu3r2LPXv24ODBg0hJSYGnpyfGjh2L4OBgAEBwcDCmT5+OlStXolmzZrh69SpGjRqFKlWqwMPDI881p02bhtu3b+Pw4cMwNjbG3bt3kZ6eLjp2ohKj4hszUgny8PAQevfuLQiCIMjlciE0NFTQ1tYWJk2apNhfo0YNITMzU3HM9u3bBSsrK0EulyvKMjMzhUqVKglHjx4VBEEQzMzMhKCgIMX+7OxsoWbNmoprCYLyXVajo6MFAEJoaGi+cb676++rV68UZRkZGULlypWFc+fOKdX19PQUBg4cKAiCIAQEBAg2NjZK+/39/fOc678ACPv373/v/gULFggtWrRQvJ4xY4agqakpPH78WFF2+PBhQUNDQ4iLixMEQRDq1asn7Ny5U+k8c+bMERwdHQVBEISYmBgBgHD16lVBEAShV69ewogRI94bA1Fpw56HmgkJCUHVqlWRnZ0NuVyOQYMGYebMmYr9tra2SvMc165dw927d6Grq6t0noyMDNy7dw/JycmIi4uDg4ODYl+FChXQsmXLPENX77x7QFCHDh0KHPfdu3fx5s0bdO3aVak8KysLzZo1AwBERkYqxQGgUA/p2r17N5YvX4579+4hNTUVOTk50NPTU6pTu3ZtfPLJJ0rXkcvliI6Ohq6uLu7duwdPT0+MGjVKUScnJwf6+vr5XvObb75Bv379cOXKFbi6usLd3R1t27YVHTtRSWHyUDOdOnXCmjVroKWlBXNzc1SooPxPoEqVKkqvU1NT0aJFC8VwzL9Vr169UDEU5mFNqampAIDff/9d6Y828HYeRyphYWEYPHgwZs2aBTc3N+jr62PXrl1YtGiR6Fh//PHHPMnsfU/V6969Ox4+fIhDhw4hNDQUXbp0gZeXFxYuXFj4xhAVIyYPNVOlShXF87ILonnz5ti9ezdMTEzyfPp+x8zMDBcuXICzszOAt5+ww8PDlZ5p/W+2traQy+U4ffo0XFxc8ux/1/PJzc1VlNnY2EBbWxuxsbHv7bFYW1srJv/fOX/+/Mcb+S/nzp2DhYUFvv/+e0XZw4cP89SLjY3F06dPYW5urriOhoYGrKysUKNGDZibm+P+/fsYPHhwga9dvXp1eHh4wMPDA05OTvDz82PyoFKLq63ogwYPHgxjY2P07t0bf/75J2JiYnDq1Cl8++23ePz4MQBg/PjxmD9/Pg4cOICoqCiMHTv2g9/RqFOnDjw8PDBy5EgcOHBAcc49e/YAACwsLCCTyRASEoLnz58jNTUVurq6mDRpEnx8fLB161bcu3cPV65cwYoVK7B161YAwJgxY3Dnzh34+fkhOjoaO3fuxJYtW0S1t0GDBoiNjcWuXbtw7949LF++HPv3789TT0dHBx4eHrh27Rr+/PNPfPvtt+jfvz9MTU0BALNmzUJgYCCWL1+Ov//+Gzdu3MDmzZuxePHifK87ffp0/Prrr7h79y5u3bqFkJAQWFtbi4qdqESpetKFSs6/J8zF7I+LixOGDRsmGBsbC9ra2kLdunWFUaNGCcnJyYIgvJ0gHz9+vKCnpycYGBgIvr6+wrBhw947YS4IgpCeni74+PgIZmZmgpaWllC/fn1h06ZNiv2zZ88WTE1NBZlMpng8rlwuF5YuXSpYWVkJFStWFKpXry64ubkJp0+fVhx38OBBoX79+oK2trbg5OQkbNq0SfSEuZ+fn2BkZCRUrVpV+PLLL4UlS5YI+vr6iv0zZswQ7O3thdWrVwvm5uaCjo6O8PnnnwuJiYlK5w0ODhaaNm0qaGlpCYaGhoKzs7Owb98+QRDyTpjPmTNHsLa2FipVqiRUq1ZN6N27t3D//v33xkykanwMLRERicZhKyIiEo3Jg4iIRGPyICIi0Zg8iIhINCYPIiISjcmDiIhEY/IgIiLRmDyIiEg0Jg8iIhKNyYOIiERj8iAiItGYPIiISLT/A5vePOeFxjfKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[  150,    18,     0,     0,     3],\n",
       "       [    7, 19674,     0,     5,    13],\n",
       "       [    0,     0,     0,    12,     0],\n",
       "       [    0,     1,     0,   503,    38],\n",
       "       [    0,    23,     0,    25,  1416]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "def plotHeatMap(Y_test, Y_pred):\n",
    "    con_mat = confusion_matrix(Y_test, Y_pred)\n",
    "    # 绘图\n",
    "    plt.figure(figsize=(4, 5))\n",
    "    seaborn.heatmap(con_mat, annot=True, fmt='.20g', cmap='Blues')\n",
    "    plt.ylim(0, 5)\n",
    "    plt.xlabel('Predicted labels')\n",
    "    plt.ylabel('True labels')\n",
    "    plt.show()\n",
    "print(model1.shape)\n",
    "# print(X_test.shape)\n",
    "# plotHeatMap(Y_test, model1)\n",
    "# X_test.shape\n",
    "\n",
    "one_hot_preds = keras.utils.to_categorical(model1.argmax(axis=1), num_classes=5)\n",
    "print(one_hot_preds)\n",
    "\n",
    "# categories = 5 # 类别数目\n",
    "# # 创建单位矩阵，并获取对应行\n",
    "# one_hot_matrix = np.eye(categories)\n",
    "# one_hot_data = one_hot_matrix[Y_test]\n",
    "# print(one_hot_data)\n",
    "one_hots = to_categorical(Y_test)\n",
    "one_hots\n",
    "data = [np.argmax(one_hot)for one_hot in one_hots]\n",
    "data_pre = [np.argmax(one_hot)for one_hot in one_hot_preds]\n",
    "print(len(Y_test))\n",
    "print(data_pre)\n",
    "plotHeatMap(data,data_pre)\n",
    "confusion_matrix(data, data_pre)"
   ]
  }
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